Paper Contents
Abstract
The increasing integration of AI in healthcare decision-making and other sectors has sparked significant concerns regarding fairness and bias within these systems. These concerns are particularly pronounced in critical domains like healthcare, employment, criminal justice, credit scoring, and the emerging use of generative AI (GenAI), which creates synthetic media. Such systems can result in inequitable outcomes and exacerbate existing social disparities. Generative biases in AI may distort the representation of individuals in synthetic data, amplifying societal inequalities. This paper provides a concise yet comprehensive analysis of fairness and bias in AI, addressing their sources, impacts, and mitigation strategies.
Copyright
Copyright © 2024 SANJEEV RANJAN. This is an open access article distributed under the Creative Commons Attribution License.
